Agent-based Federated Hybrid Cloud Prof. Yue-Shan Chang Distributed & Mobile Computing Lab. Dept. of Computer Science & Information Engineering National Taipei University

Cloud computing: – The evolution and convergence of computing trends – Layers SaaS: Software As A Service PaaS: Platform As A Service IaaS: Infrastructure As A Service Introduction 2015/9/212

Introduction Types – Private Cloud each enterprise’s IT platform has their own network, servers and storage hardware (Data Centers) – Public Cloud User can obtain any service and resource from service provider pay-per-use charging model

Introduction 2015/9/214 Which one is suitable? –Considering Issues Cost (Construction, Operation, Maintenance, Tax …) Security (Data, Network, …) Flexibility & Convenience (Operation, Maintenance, Management, …) Reliability & Availability Performance

Main benefits of using a public cloud service: – Easy and inexpensive set-up because hardware, application and bandwidth costs are covered by the provider. – Scalability to meet needs. – No wasted resources because you pay for what you use. Introduction 2015/9/215

Introduction 2015/9/216 6 What kind of cloud do I need? Private? Public?

A hybrid cloud – is a cloud computing environment in which an organization provides and manages some resources in-house and has others provided externally. Introduction 2015/9/217 HiClou d Amazo n Google Public Cloud Private Cloud

Effectively utilize public cloud resource is an important issue while adopting hybrid cloud – what kind of jobs need to be dispatched or be migrated to public cloud? – When does a job be dispatched to public cloud? – And how will a job be dispatched to public cloud? Introduction 2015/9/218

Hybrid Cloud Project – ITRI Cloud Center – f5 Hybrid Cloud Architecture director.pdf – Fujitsu Hybrid Cloud Mikio Funahashi, Shigeo Yoshikawa “Fujitsu’s Approach to Hybrid Cloud Systems,” Fujitsu Sci. Tech. J., Jul. 2011, Vol. 47, No.3, pp – IBM Hybrid Cloud IBM Service Management Extensions for Hybrid Cloud en/IBD03004USEN.PDF Introduction 2015/9/219

ITRI Hybrid Cloud Architecture Introduction 2015/9/2110 Public Cloud Private Cloud

f5 Hybrid Cloud Architecture – Introduction 2015/9/2111

Fujitsu’s Approach to Hybrid Cloud Systems – Mikio Funahashi, Shigeo Yoshikawa “Fujitsu’s Approach to Hybrid Cloud Systems,” Fujitsu Sci. Tech. J., Jul. 2011, Vol. 47, No.3, pp Introduction 2015/9/2112

Agent & Grid Computing – Ian Foster addressed that agent technology and grid computing need each other because agent technology can enhance the ability of problem solving of grid. Agent & Cloud computing – More and more research adopting agent technology to solve problems faced in the cloud Introduction 2015/9/2113

Propose an automatic, intelligent framework based on agent technology. A federated layer to tie private and public cloud. Mobile agent technique is exploited – manage all resources, – monitor system behaviour, – negotiate all actions Introduction 2015/9/2114

Objective – For performance issue Load balance – For cost issue utilize private cloud as much as possible if private cloud cannot complete user’s job before deadline (Deadline-constraint Job) – dispatch the job to public cloud » minimize the required resource of the VM Introduction 2015/9/2115

Agent-based Federated Broker 2015/9/2116

Agent-based Federated Broker Five major components – System Monitoring Agent (SMA) Collects the system information – Reconfiguration Decision Agent (RDA) Reconfigure and adjust the cloud environment. – Service Dispatching Agent (SDA) assign a location in the cloud that allows the job to be executed on. if some clusters are overloading, SDA will notify some JAs to migrate to some other cluster, to balance the load. 2015/9/2117

Agent-based Federated Broker – Cluster Management Agent (CMA) schedules jobs locally in a FCFS fashion, so that there is only one job is executing on the cluster. reports the status of the cluster collects the information and send it via heartbeats to SeMA. – Job Agent (JA) encapsulates a job, the job can be migrated along with the JA. executes and monitors the job on the cluster. reports the job status to the CMA periodically. brings the results back to the private cloud. 2015/9/2118

Job dispatching – Pack a job into Job Agent(JA) – dispatching JA to destination – Unpack the JA Agent-based Federated Broker 2015/9/2119

Job Count (JC), – T c : Job count threshold the SMA will pick up the (JC PU +T C +1) th job from job queue of private cloud, and trigger it to be migrated. For example, if the JC PR is equal to 10, the JC PU is equal to 4, and the T C is equal to 2. Therefore, the 7 th job will be migrated to public cloud. Policy of Job Dispatching to Public Cloud 2015/9/2120

total Size of Job (SJ), – the SMA will pick up the th job from the job queue of private cloud, and trigger it to be migrated. For example, if the total size of job in public cloud is 10Mbytes, the T S is equal to 2Mbytes, and the size of jobs in private cloud are 3, 4, 3, 3, 2, 3, 4 Mbytes respectively. The 5 th job (2Mbytes) will be migrated to public cloud because the ( ); so that the 5 th job will be migrated. Policy of Job Dispatching to Public Cloud 2015/9/2121 T s : the threshold of SJ

Estimated Finish Time (EFT) – the SMA will pick the th job in the queue of private cloud, and trigger it to be migrated. For example, if the total finish time of jobs in public cloud is 100s, the T T is equal to 20s, and the finish time of jobs in private cloud are 33, 24, 45, 43, 22, 37, 24 second respectively. The 5 th job (22s of finish time) will be migrated to public cloud – Rough Set Theory Policy of Job Migration 2015/9/2122

Agent Platform for the hybrid cloud Prototyping and evaluation 2015/9/2123

Job migrated Prototyping and evaluation 2015/9/2124

Comparison between with migration and without migration Evaluation 2015/9/2125

Comparison between job count and total size of job Evaluation 2015/9/2126

an agent-based automatic intelligent job migration framework on a hybrid cloud is proposed. – built a prototype that integrating our private cloud with public cloud. We demonstrate the job migration mechanism on Hadoop platform – it shows that the framework can be applied to hybrid cloud and work well. Summary 2015/9/2127

Execution Time Prediction Using Rough Set Theory in Hybrid Cloud

Introduction Resource utilization is important issue in cloud computing – Could the remaining resource in private cloud serve the incoming task and complete the task before deadline? – If not, the incoming task need to be dispatched to public cloud. How much resource we need to preserve to serve the deadline-constraint task in public cloud? For the remaining resource, the execution time prediction of a task becomes an important issue in hybrid cloud. 2015/9/2129

Introduction Exploit Rough Set Theory (RST) to predict job's execution time in the hybrid cloud environment. – RST is a well-known prediction technique that uses the historical data to predict the attribute value of an object. – We propose an execution time prediction algorithm based on RST to schedule jobs The evaluation show that the RST can be utilized to accurately predict the execution time while historical data is increasingly. 2015/9/2130

RST-based Prediction Rough Set Theory (RST) – have been witnessed that is a useful prediction technique based on historical data in a variety of applications, such as quantitative structure–activity relationship in the Chemistry and data mining. – It provides an appropriate theory for identifying good “similarity templates”. The primary objective of similarity templates is to identify characteristics of applications that define similarity. Two prediction phases – Inference rule deducing phase – Estimation phase 2015/9/2131

RST-based Prediction Inference rule deducing phase – Steps (detailed methodology of RST can refer to [2]) Define all attributes; including condition attributes (CA) and decision attributes (DA). Discretize the properties of historical records for diversified attributes. Calculate D-Reducts – Utilize discernibility matrix to list all properties, – apply discernibility function to formulate the relation of the properties, – and then simplify the formulation using boolean algebra. Derive the inference rule of DA /9/2132

RST-based Prediction Define all attributes 2015/9/2133 Decision Attribute Conditional Attributes

RST-based Prediction Discretize the properties of historical records 2015/9/2134

RST-based Prediction Calculate D-Reducts and D-Core – Generate discernibility matrix 2015/9/2135

RST-based Prediction Calculate D-Reducts and D-Core – Formulate discernibility function: f A (D) 2015/9/2136 Both {a 1, a 3 } and {a 2,a 3 } are D-Reducts, {a 3 } is D-core

RST-based Prediction Calculate D-Reducts and D-Core – formulate the relation of the properties, and simplify the formulation 2015/9/2137 f 2 (D)=a 1, f 3 (D)=a 1 +a 3, f 4 (D)=a 1 +a 3, …

RST-based Prediction Deduce Inference Rule (- : means don’t care) – a 1 =2 -> d=2 – a 1 =3-> d=1 – a 3 =4 -> d=4 – a 1 =1 and a 3 =2 -> d=2 2015/9/2138

RST-based Prediction Estimation Phase – Apply simple mathematical operation, such as arithmetic average of the value of DA, to obtain the final value of the DA. » Estimated time = (job3+job5+job6)/3 2015/9/2139 ElementProcessor Speed Input sizeExecution time The new job52?

Prototyping and Evaluation Prototype the system using the agent platform JADE v /9/2140

Prototyping and Evaluation two jobs are submitted to the system – Compute π – Area Approximation 2015/9/2141

Prototyping and Evaluation The Error Rate – Positive->over prediction, – Negative->under predicted. Vibration during the first 25 jobs. lack of the historical data that can be used to predict the job. – The more the historical data are stored, the more accurate the prediction will be. 2015/9/2142

Prototyping and Evaluation Absolute Error Rate. – shows how much improvement has the prediction made. The higher the absolute error is, the more improvement is needed. – for 2 kinds of jobs with 200 submissions are and – the accuracy is very impressive if remove the first 25 predictions 2015/9/2143

Prototyping and Evaluation The largest prediction latency is ms with 190 jobs – is acceptable. no new record to be updated, the prediction time taken can be less than 1 ms. generating the decision rule needs much more time than just predicting the value. To reduce the time of predicting, – periodically updating the decision rules can be considered. 2015/9/2144

Summary we utilized the RST to predict the execution time in hybrid cloud. The result shows that RST-based predictor can predict the execution time of a job – error rate under 0.1 when the number of historical job is over 50. – When more records available, the error rate can drop under Latency is reasonable, – less than 1 second with 190 historical records to perform a full prediction. The system can aid users to schedule their jobs faster and more accurate. 2015/9/2145

Please refer to – Demo 2015/9/2146